SaMfENet: Self-Attention Based Multi-Scale Feature Fusion Coding and Edge Information Constraint Network for 6D Pose Estimation

Accurate estimation of an object’s 6D pose is one of the crucial technologies for robotic manipulators. Especially when the lighting conditions changes or the object is occluded, resulting in the missing or the interference of the object information, which makes the accurate 6D pose estimation more...

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Bibliographic Details
Published in:Mathematics (Basel) 2022-10, Vol.10 (19), p.3671
Main Authors: Li, Zhuoxiao, Li, Xiaobing, Chen, Shihao, Du, Jialong, Li, Yong
Format: Article
Language:English
Subjects:
6D pose estimation
Accuracy
Algorithms
attention mechanism
Clustering
Coding
Coding theory
Constraint satisfaction
Datasets
Deep learning
Design and construction
edge information constraint
Estimation theory
Feature extraction
Food science
Machine vision
Manipulators
Mathematical research
Methods
Modules
multi-scale feature fusion
Neural networks
Pose estimation
Robot arms
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Summary:Accurate estimation of an object’s 6D pose is one of the crucial technologies for robotic manipulators. Especially when the lighting conditions changes or the object is occluded, resulting in the missing or the interference of the object information, which makes the accurate 6D pose estimation more challenging. To estimate the 6D pose of the object accurately, a self-attention-based multi-scale feature fusion coding and edge information constraint 6D pose estimation network is proposed, which can achieve accurate 6D pose estimation by employing RGB-D images. The proposed algorithm first introduces the edge reconstruction module into the pose estimation network, which improves the attention of the feature extraction network to the edge features. Furthermore, a self-attention multi-scale point cloud feature extraction module, i.e., MSPNet, is proposed to extract point cloud geometric features, which are reconstructed from depth maps. Finally, the clustering feature encoding module, i.e., SE-NetVLAD, is proposed to encode multi-modal dense feature sequences to construct more expressive global features. The proposed method is evaluated on the LineMOD and YCB-Video datasets, and the experimental results illustrate that the proposed method has an outstanding performance, which is close to the current state-of-the-art methods.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10193671